Part 1: PCA with penguins

penguin_pca <- penguins %>% 
  select(body_mass_g, ends_with("_mm")) %>% 
  drop_na() %>% 
  scale() %>% 
  prcomp()

penguin_pca$rotation
##                          PC1         PC2        PC3        PC4
## body_mass_g        0.5483502 0.084362920 -0.5966001 -0.5798821
## bill_length_mm     0.4552503 0.597031143  0.6443012 -0.1455231
## bill_depth_mm     -0.4003347 0.797766572 -0.4184272  0.1679860
## flipper_length_mm  0.5760133 0.002282201 -0.2320840  0.7837987
penguin_complete <- penguins %>% 
  drop_na(body_mass_g, ends_with("_mm"))

autoplot(penguin_pca,
         data = penguin_complete,
         colour = 'species',
         loadings = TRUE,
         loadings.label = TRUE) +
  theme_minimal()
## Warning: `select_()` is deprecated as of dplyr 0.7.0.
## Please use `select()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Part 2: ggplot2 customzation & reading in different file types

REad in a .xlsx file & do some wrangling

fish_noaa <- read_excel(here("data","foss_landings.xlsx")) %>% 
  clean_names() %>% 
  mutate(across(where(is.character), tolower)) %>% 
  mutate(nmfs_name = str_sub(nmfs_name, end = -4)) %>% 
  filter(confidentiality == "public")

Make a customized graph:

fish_plot <- ggplot(data = fish_noaa, aes(x = year, y = pounds)) +
  geom_line(aes(color = nmfs_name), show.legend = FALSE) +
  theme_minimal()

fish_plot
## Warning: Removed 6 row(s) containing missing values (geom_path).

ggplotly(fish_plot)